Abstract
Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossible to use conventional network analysis methods. Herein, we employ artificial intelligence (specifically swarm computing), to compute centrality metrics in a completely decentralized fashion. More exactly, we show that by overlaying a homogeneous artificial system (inspired by swarm intelligence) over a complex network (which is a heterogeneous system), and playing a game in the fused system, the changes in the homogeneous system will reflect perfectly the complex network properties. Our method, dubbed Game of Thieves (GOT), computes the importance of all network elements (both nodes and edges) in polylogarithmic time with respect to the total number of nodes. Contrary, the state-of-the-art methods need at least a quadratic time. Moreover, the excellent capabilities of our proposed approach, it terms of speed, accuracy, and functionality, open the path for better ways of understanding and controlling complex networks.
Highlights
Almost all the natural or human made systems can be understood and controlled using complex networks
We overlay a homogeneous artificial system over a complex network, which is a heterogeneous system - its level of heterogeneity being given by its topology
Each node is endowed with wandering thieves, mobile actors which act stochastic and egoistic
Summary
Almost all the natural or human made systems can be understood and controlled using complex networks. Unveiling the complex networks hidden patterns and computing even their most basic properties is far from trivial, due to the massive number of node entangles that interact in non-obvious ways, evolving and unfolding continuously[15] Among all these network properties, the centrality (or importance) of nodes and links is fundamental to understanding things such as: biological neural networks[2,3,4], cosmic structures[5], biological networks[7], how viruses spread or can be contained[16]; which people or news are influencing opinions and decisions the most[17]; how to protect computer systems from cyber-attacks[18]; or how to relay data packets in the one-trillion Internet-of-Things network of the future. This easy to obtain volume can be used to measure other properties of the object, e.g. density
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